Page 30 - Environment: The Science Behind the Stories

Develop a hypothesis Scientists address their questions
by devising explanations that they can test. A hypothesis is a
statement that attempts to explain a phenomenon or answer a
scientific question. For example, a scientist investigating the
question of why algae are growing excessively in local ponds
might observe that chemical fertilizers are being applied on
farm fields nearby. The scientist might then propose a hypoth-
esis as follows: “Agricultural fertilizers running into ponds
cause the amount of algae in the ponds to increase.”

Make predictions The scientist next uses the hypothesis
to generate predictions, specific statements that can be directly
and unequivocally tested. In our algae example, a researcher
might predict: “If agricultural fertilizers are added to a pond, the
quantity of algae in the pond will increase.”

Test the predictions Scientists test predictions by gath-
ering evidence that could potentially refute them and thus dis-
prove the hypothesis. The strongest form of evidence comes
from experimentation. An experiment is an activity designed
to test the validity of a prediction or a hypothesis. It involves
manipulating variables, or conditions that can change.
For example, a scientist could test the prediction linking
algal growth to fertilizer by selecting two identical ponds and
adding fertilizer to one of them. In this example, fertilizer input
is an independent variable, a variable the scientist manipulates,
whereas the quantity of algae that results is the dependent vari-
able, one that depends on the fertilizer input. If the two ponds
are identical except for a single independent variable (fertilizer
input), then any differences that arise between the ponds can be
attributed to that variable. Such an experiment is known as a con-
trolled experiment because the scientist controls for the effects
of all variables except the one he or she is testing. In our exam- FIGURE 1.10 Researchers gather data in order to test predic-
ple, the pond left unfertilized serves as a control, an unmanipu- tions in experiments. Here, Dr. Jennifer Smith of the Scripps
lated point of comparison for the manipulated treatment pond. Institution of Oceanography in San Diego photographs coral at a
Whenever possible, it is best to replicate one’s experi- remote reef in the South Pacific. Data from analysis of the photos
ment; that is, to stage multiple tests of the same comparison. will help her test hypotheses about how human impacts affect the
Our scientist could perform a replicated experiment on, say, condition and community structure of coral reefs.
10 pairs of ponds, adding fertilizer to one of each pair. CHAPTER 1 • SCIENCE AND SUSTAIN ABILITY : AN INTR ODUCTI ON T O ENVIR ONMENTAL SCIENCE
scientists can determine objectively and precisely the strength
Analyze and interpret results Scientists record data, or and reliability of patterns they find.
information, from their studies (FIGURE 1.10). They particularly Some research, especially in the social sciences, involves
value quantitative data (information expressed using numbers), data that is qualitative, or not expressible in terms of num-
because numbers provide precision and are easy to compare. bers. Research involving historical texts, personal interviews,
The scientist running the fertilization experiment, for instance, surveys, case studies, or descriptive observation of behavior
might quantify the area of water surface covered by algae in can include qualitative data on which quantitative statistical
each pond or might measure the dry weight of algae in a certain analysis may not be possible.
volume of water taken from each. It is vital, however, to collect If experiments disprove a hypothesis, the scientist will
data that is representative. Because it is impractical to meas- reject it and may formulate a new hypothesis to replace it. If
ure a pond’s total algal growth, our researcher would instead experiments fail to disprove a hypothesis, this lends support
sample from multiple areas of the pond. These areas must be to the hypothesis but does not prove it is correct. The scientist
selected in a random manner, since choosing areas with the may choose to generate new predictions to test the hypoth-
most growth or the least growth, or areas most convenient to esis in different ways and further assess its likelihood of being
sample, would not provide a representative sample. true. Thus, the scientific method loops back on itself, often
Even with the precision that numbers provide, a scien- giving rise to repeated rounds of hypothesis revision and new
tist’s results may not be clear-cut. Data from treatments and experimentation (see Figure 1.9).
controls may vary only slightly, or replicates may yield dif- If repeated tests fail to reject a hypothesis, evidence in
ferent results. The researcher must therefore analyze the data favor of it accumulates, and the researcher may eventually con-
using statistical tests. With these mathematical methods, clude that the hypothesis is well supported. Ideally, the scientist 29

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